Optimal sample size and censoring scheme in progressively type II censoring based on Fisher information for the Pareto distribution

One of the most common censoring methods is the progressive type-II censoring. In this method of censoring, a total of $n$ units are placed on the test, and at the time of failure of each unit, some of the remaining units are randomly removed. This will continue to record $m$ failure times, where $m$ is a pre-determined value, and then the experiment ends. The problem of determining the optimal censoring scheme in the progressive type-II censoring has been studied so far by considering different criteria. Another issue in the progressive type-II censoring is choosing the sample size at the start of the experiment, namely $n$. In this paper, assuming the Pareto distribution for the data, we will determine the optimal sample size, $n_ {opt}$, as well as the optimal censoring scheme by means of the Fisher Information. Finally, to evaluate the results, numerical calculations have been presented by using $R$ software.